Coupling BM3D with directional wavelet packets for image denoising
Amir Averbuch, Pekka Neittaanmaki, Valery Zheludev, Moshe Salhov and, Jonathan Hauser

TL;DR
This paper introduces a novel image denoising method that combines directional wavelet packets with BM3D, iteratively enhancing denoising performance by leveraging their complementary strengths.
Contribution
It proposes a cross-boosting iterative algorithm that integrates directional wavelet packet-based denoising with BM3D, achieving superior results over existing methods.
Findings
Outperforms state-of-the-art algorithms in denoising quality
Effectively captures edges and textures in images
Achieves competitive or superior results in experiments
Abstract
The paper presents an image denoising algorithm by combining a method that is based on directional quasi-analytic wavelet packets (qWPs) with the popular BM3D algorithm. The qWPs and its corresponding transforms are designed in [1]. The denoising algorithm qWP (qWPdn) applies an adaptive localized soft thresholding to the transform coefficients using the Bivariate Shrinkage methodology. The combined method consists of several iterations of qWPdn and BM3D algorithms, where the output from one algorithm updates the input to the other (cross-boosting).The qWPdn and BM3D methods complement each other. The qWPdn capabilities to capture edges and fine texture patterns are coupled with utilizing the sparsity in real images and self-similarity of patches in the image that is inherent in the BM3D. The obtained results are quite competitive with the best state-of-the-art algorithms. We compare…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage and Signal Denoising Methods · Advanced Image Fusion Techniques · Advanced Image Processing Techniques
